Ricardo S. Alonso

Agents and ambient intelligence: case studies

The significance that ambient intelligence (AmI) has acquired in recent years requires the development of innovative solutions. In this sense, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. One of the most important aspects in AmI is the use of context-aware technologies in order to perceive stimuli from both the users and the environment. Thus, the information obtained must be managed by intelligent and self-adaptable technologies in order to provide an adequate interaction between the users and their environment. Agents and multi-agent systems are one of these technologies. The agents have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing dynamic and distributed systems based on AmI, as they possess the capability of adapting themselves to the users and environmental characteristics. This paper presents several case studies where agents and context-aware technologies have been implemented to build AmI-based systems. These case studies expand the possibilities of AmI and get closer to its vision.

Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems

Ambient Intelligence (AmI) systems require the integration of complex and innovative solutions. In this sense, agents and multi-agent systems have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing distributed systems based on Ambient Intelligence. In addition, the use of context-aware technologies is an essential aspect in these developments in order to perceive stimuli from the context and react to it autonomously. This paper presents the integration of the Hardware-Embedded Reactive Agents (HERA) Platform into the Flexible and User Services Oriented Multi-agent Architecture (FUSION@), a multi-agent architecture for developing AmI systems that integrates intelligent agents with a service-oriented architecture approach. Because of this integration, FUSION@ has the ability to manage both software and hardware agents by using self-adaptable heterogeneous wireless sensor networks. Preliminary results presented in this paper demonstrate the feasibility of FUSION@ as a future alternative for developing Ambient Intelligence systems where users and systems can use both software and hardware agents in a transparent way, achieving a higher level of ubiquitous computing and communication.

Introducing a Distributed Architecture for Heterogeneous Wireless Sensor Networks

This paper presents SYLPH, a novel distributed architecture which integrates a service-oriented approach into Wireless Sensor Networks. One of the characteristics of SYLPH is that it can be executed over multiple wireless devices independently of their microcontroller or the programming language they use. SYLPH works in a distributed way so that most of the application code does not have to reside in a central node. Furthermore, SYLPH allows the interconnection of several networks from different wireless technologies, such as ZigBee or Bluetooth. This paper focuses on describing the main components of SYLPH and the issues that lead to design and develop this new approach. Results and conclusions are presented after evaluating a preliminary version of this architecture.

Mitigation of the ground reflection effect in real-time locating systems based on wireless sensor networks by using artificial neural networks

Wireless sensor networks (WSNs) have become much more relevant in recent years, mainly because they can be used in a wide diversity of applications. Real-time locating systems (RTLSs) are one of the most promising applications based on WSNs and represent a currently growing market. Specifically, WSNs are an ideal alternative to develop RTLSs aimed at indoor environments where existing global navigation satellite systems, such as the global positioning system, do not work correctly due to the blockage of the satellite signals. However, accuracy in indoor RTLSs is still a problem requiring novel solutions. One of the main challenges is to deal with the problems that arise from the effects of the propagation of radiofrequency waves, such as attenuation, diffraction, reflection and scattering. These effects can lead to other undesired problems, such as multipath. When the ground is responsible for wave reflections, multipath can be modeled as the ground reflection effect. This paper presents an innovative mathematical model for improving the accuracy of RTLSs, focusing on the mitigation of the ground reflection effect by using multilayer perceptron artificial neural networks.

Ambient intelligence and collaborative e-learning: a new definition model

The constant evolution of mobile devices and Information Technologies allows users to enjoy a new variety of features that were unimaginable some years ago. In this sense, Ambient Intelligence (AmI) has emerged as a new discipline focused on people and aimed at facilitating their daily activities. This paper proposes a new definition model that helps designers to characterize collaborative e-learning systems based on AmI and Computer Supported Collaborative Learning. The use of mobile devices and Mobile Adhoc Networks is a key aspect in this model as they allow users to access resources from anywhere on demand. The proposed model is applied to a concrete case of study as an example of its application on real scenarios.

ARTIZT: Applying Ambient Intelligence to a Museum Guide Scenario

Museum guides present a great opportunity where the Ambient Intelligence (AmI) paradigm can be successfully applied. Together with pervasive computing, context and location awareness are the AmI features that allow users to receive customized information in a transparent way. In this sense, Real-Time Locating Systems (RTLS) can improve context-awareness in AmI-based systems. This paper presents ARTIZT, an innovative AmI-based museum guide system where a novel RTLS based on the ZigBee protocol provides highly precise users’ position information. Thus, it can be customized the content offered to the users without their explicit interaction, as well as the granularity level provided by the system.

ALZ-MAS 2.0; A Distributed Approach for Alzheimer Health Care

This paper presents ALZ-MAS 2.0; an Ambient Intelligence based multi-agent system aimed at enhancing the assistance and health care for Alzheimer patients living in geriatric residences. The system makes use of FUSION@, a multi-agent architecture which facilitates the integration of distributed services and applications to optimize the construction of Ambient Intelligence environments. The architecture optimizes the development of distributed multi-agent systems, where applications and services can communicate in a distributed way, even from mobile devices, independent of a specific programming language or operating system. The results obtained demonstrate that ALZ-MAS 2.0 is far more robust and has better performance than the previous version.

Energy Efficiency in Public Buildings through Context-Aware Social Computing

The challenge of promoting behavioral changes in users that leads to energy savings in public buildings has become a complex task requiring the involvement of multiple technologies. Wireless sensor networks have a great potential for the development of tools, such as serious games, that encourage acquiring good energy and healthy habits among users in the workplace. This paper presents the development of a serious game using CAFCLA, a framework that allows for integrating multiple technologies, which provide both context-awareness and social computing. Game development has shown that the data provided by sensor networks encourage users to reduce energy consumption in their workplace and that social interactions and competitiveness allow for accelerating the achievement of good results and behavioral changes that favor energy savings.

SYLPH: An Ambient Intelligence based platform for integrating heterogeneous Wireless Sensor Networks

The significance that Ambient Intelligence (AmI) has acquired in recent years requires the development of innovative solutions. Nonetheless, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. In this regard, the use of context-aware technologies is an essential aspect in these developments to perceive stimuli from the context and react upon it autonomously. This work presents a novel platform that defines a method for integrating dynamic and self-adaptable heterogeneous Wireless Sensor Networks (WSNs). This approach facilitates the inclusion of context-aware capabilities when developing intelligent ubiquitous systems, where functionalities can communicate in a distributed way. Furthermore, the information obtained must be managed by intelligent and self-adaptable technologies to provide an adequate interaction between the users and their environment. Agents and Multi-Agent Systems are one of these technologies. The agents have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing dynamic and distributed systems based on AmI. This way, the integration of the platform with a Service-Oriented Multi-Agent architecture is proposed. Finally, conclusions and future work are presented.

Cloud-IO: Cloud Computing Platform for the Fast Deployment of Services over Wireless Sensor Networks

In the recent years, a new computing model, known as Cloud Computing, has emerged to react to the explosive growth of the number of devices connected to Internet. Cloud Computing is centered on the user and offers an efficient, secure and elastically scalable way of providing and acquiring services. Likewise, Ambient Intelligence (AmI) is also an emerging paradigm based on ubiquitous computing that proposes new ways of interaction between humans and machines, making technology adapt to the users’ necessities. One of the most important aspects in AmI is the use of context-aware technologies such as Wireless Sensor Networks (WSN) to perceive stimuli from both the users and the environment. In this regard, this paper presents Cloud-IO, a Cloud Computing platform for the fast integration and deployment of services over WSNs.